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    <abstract>Zero-Shot Relation Extraction (ZSRE) aims to predict unseen relations for given entity pairs in sentences. Existing methods typically operate from a local perspective, predicting the relation for each entity pair (given its corresponding sentence) in isolation. Consequently, they often fail to distinguish between unseen, semantically similar relations, particularly when the sentence phrasing is ambiguous.To address this limitation, we propose **G-NSR**, a novel ZSRE framework built upon a **G**lobal **N**euro-**S**ymbolic **R**easoner architecture, specifically designed to enable global reasoning across a set of predictions. The key idea is to model the logical relationships among multiple predictions, and perform neuro-symbolic reasoning to ensure logically consistent and more accurate predictions. Specifically, we first introduce Duality Type-Constrained Relation Schemas, which formulate each candidate relation as a pair of complementary positive-negative propositions. These propositions are then synthesized by our designed Neuro-Symbolic Reasoner, which explicitly models their logical interdependencies. By approximating logical rules, the reasoner allows high-confidence predictions to serve as evidence for refining incorrect results, ensuring the final predictions are logically consistent and more accurate. Extensive experiments on widely used datasets demonstrate that our method significantly outperforms existing approaches and establishes new state-of-the-art results across all evaluation settings. Our code is available at https://anonymous.4open.science/r/G-NSR</abstract>
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%0 Conference Proceedings
%T From Local Perspective to Global Reasoning: A Neuro-Symbolic Framework for Zero-Shot Relation Extraction
%A Lyu, Kailun
%A Zhang, Fu
%A Li, Zehan
%A Cheng, Jingwei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lyu-etal-2026-local
%X Zero-Shot Relation Extraction (ZSRE) aims to predict unseen relations for given entity pairs in sentences. Existing methods typically operate from a local perspective, predicting the relation for each entity pair (given its corresponding sentence) in isolation. Consequently, they often fail to distinguish between unseen, semantically similar relations, particularly when the sentence phrasing is ambiguous.To address this limitation, we propose **G-NSR**, a novel ZSRE framework built upon a **G**lobal **N**euro-**S**ymbolic **R**easoner architecture, specifically designed to enable global reasoning across a set of predictions. The key idea is to model the logical relationships among multiple predictions, and perform neuro-symbolic reasoning to ensure logically consistent and more accurate predictions. Specifically, we first introduce Duality Type-Constrained Relation Schemas, which formulate each candidate relation as a pair of complementary positive-negative propositions. These propositions are then synthesized by our designed Neuro-Symbolic Reasoner, which explicitly models their logical interdependencies. By approximating logical rules, the reasoner allows high-confidence predictions to serve as evidence for refining incorrect results, ensuring the final predictions are logically consistent and more accurate. Extensive experiments on widely used datasets demonstrate that our method significantly outperforms existing approaches and establishes new state-of-the-art results across all evaluation settings. Our code is available at https://anonymous.4open.science/r/G-NSR
%U https://aclanthology.org/2026.findings-acl.1492/
%P 29844-29856

Markdown (Informal)

[From Local Perspective to Global Reasoning: A Neuro-Symbolic Framework for Zero-Shot Relation Extraction](https://aclanthology.org/2026.findings-acl.1492/) (Lyu et al., Findings 2026)

• From Local Perspective to Global Reasoning: A Neuro-Symbolic Framework for Zero-Shot Relation Extraction (Lyu et al., Findings 2026)

ACL

• Kailun Lyu, Fu Zhang, Zehan Li, and Jingwei Cheng. 2026. From Local Perspective to Global Reasoning: A Neuro-Symbolic Framework for Zero-Shot Relation Extraction. In Findings of the Association for Computational Linguistics: ACL 2026, pages 29844–29856, San Diego, California, United States. Association for Computational Linguistics.